INTEGRATING EDGE COMPUTING AND AI FOR REAL-TIME DATA PROCESSING IN .NET-BASED APPLICATIONS: A SCALABLE SOLUTION FOR LOW-LATENCY SYSTEMS

Abstract

The integration of edge computing and artificial intelligence (AI) offers transformative potential for real-time data processing in latency-sensitive applications such as IoT, healthcare, and industrial automation. By processing data closer to its source, edge computing reduces latency and bandwidth usage, while AI provides predictive analytics and intelligent decision-making capabilities. This study presents a framework for integrating edge computing and AI within .NET-based web applications using ASP.NET Core and SignalR. The framework was evaluated under simulated IoT and industrial scenarios, demonstrating significant reductions in latency and bandwidth consumption, coupled with high AI inference accuracy and scalability. These findings underscore the potential of edge computing and AI in enabling efficient, intelligent, and real-time web applications.